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Metro, known employer and mid-senior AI role increases applicants, but niche semantic/LLM skillset limits competition.
Specialized semantic technologies, knowledge graphs and LLM engineering limit cross-industry transferability.
Explicit 6+ years plus mandatory LLM, RDF/OWL, SPARQL, and cloud/MLops requirements indicate high strictness.
Design and implement agent-driven AI pipelines using semantic ontologies, knowledge graphs, and LLMs to automate semantic mapping and data modeling at scale.
Build and maintain enterprise semantic ontologies (RDF/OWL) and AI-powered services for schema understanding and metadata generation within data platforms.
Develop and optimize dimension and fact generation pipelines on Microsoft Fabric to produce business-ready star schemas from heterogeneous data sources, while defining engineering standards and mentoring team members.
6+ years professional software engineering experience with strong proficiency in Python and GenAI.
Hands-on experience with LLM-based systems and semantic technologies: RDF, OWL, ontology modeling, knowledge graphs, and SPARQL.
Experience with agentic AI frameworks (e.g., Google ADK, LangChain agents) and data engineering concepts (ETL/ELT, star schemas).
Experience building and operating systems on cloud platforms, preferably Microsoft Azure / Microsoft Fabric.
Experienced in building scalable AI and semantic data platforms involving ontologies and knowledge graphs in enterprise contexts.
Comfortable working on greenfield platform initiatives with strong problem-solving skills and ability to establish engineering best practices.
Skilled in integrating AI/LLM technologies within data engineering workflows and collaborating with data architects and domain experts for platform adoption and scalability.